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Littérature scientifique sur le sujet « Continuous Time Bayesian Network Classifier »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Continuous Time Bayesian Network Classifier"

Stella, F., and Y. Amer. "Continuous time Bayesian network classifiers." Journal of Biomedical Informatics 45, no. 6 (2012): 1108–19. http://dx.doi.org/10.1016/j.jbi.2012.07.002.

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Codecasa, Daniele, and Fabio Stella. "Learning continuous time Bayesian network classifiers." International Journal of Approximate Reasoning 55, no. 8 (2014): 1728–46. http://dx.doi.org/10.1016/j.ijar.2014.05.005.

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Villa, S., and F. Stella. "A continuous time Bayesian network classifier for intraday FX prediction." Quantitative Finance 14, no. 12 (2014): 2079–92. http://dx.doi.org/10.1080/14697688.2014.906811.

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Wei, Chenghao, Chen Li, Yingying Liu, et al. "Causal Discovery and Reasoning for Continuous Variables with an Improved Bayesian Network Constructed by Locality Sensitive Hashing and Kernel Density Estimation." Entropy 27, no. 2 (2025): 123. https://doi.org/10.3390/e27020123.

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The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous attribute values. As a non-parametric distribution-free method, kernel density estimation (KDE) is applied in the conditional independence (CI) test. The skeleton of the BN is constructed utilizing the test based on mutual information and conditional mutual information, delineating potential relational connections between parents and children witho

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Naddaf-Sh, M.-Mahdi, SeyedSaeid Hosseini, Jing Zhang, Nicholas A. Brake, and Hassan Zargarzadeh. "Real-Time Road Crack Mapping Using an Optimized Convolutional Neural Network." Complexity 2019 (September 29, 2019): 1–17. http://dx.doi.org/10.1155/2019/2470735.

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Pavement surveying and distress mapping is completed by roadway authorities to quantify the topical and structural damage levels for strategic preventative or rehabilitative action. The failure to time the preventative or rehabilitative action and control distress propagation can lead to severe structural and financial loss of the asset requiring complete reconstruction. Continuous and computer-aided surveying measures not only can eliminate human error when analyzing, identifying, defining, and mapping pavement surface distresses, but also can provide a database of road damage patterns and th

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Hemalatha, C. Sweetlin, and V. Vaidehi. "Associative Classification based Human Activity Recognition and Fall Detection using Accelerometer." International Journal of Intelligent Information Technologies 9, no. 3 (2013): 20–37. http://dx.doi.org/10.4018/jiit.2013070102.

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Human fall poses serious health risks especially among aged people. The rate of growth of elderly population to the total population is increasing every year. Besides causing injuries, fall may even lead to death if not attended immediately. This demands continuous monitoring of human movements and classifying normal low-level activities from abnormal event like fall. Most of the existing fall detection methods employ traditional classifiers such as decision trees, Bayesian Networks, Support Vector Machine etc. These classifiers may miss to cover certain hidden and interesting patterns in the

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Procházka, Vít K., Štěpánka Matuštíková, Tomáš Fürst, et al. "Bayesian Network Modelling As a New Tool in Predicting of the Early Progression of Disease in Follicular Lymphoma Patients." Blood 136, Supplement 1 (2020): 20–21. http://dx.doi.org/10.1182/blood-2020-139830.

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Background: Twenty percent of patients (pts) with high-tumor burden follicular lymphoma (FL) develop progression/relapse of disease within 24 months of frontline immune-chemotherapy (POD24). Those ultra-high-risk cases are at 50% risk of dying within 5-years since the POD event. Unmet need is to identify such pts at the time of initial treatment. The traditional approach used for building predictive scores (such as FLIPI, PRIMA-PI) is multivariable logistic regression (LR). LR is the tool of choice in case of many predictors (continuous or categorical) and a single binary (yes/no) outcome. Bay

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Liu, Yunchuan, Amir Ghasemkhani, and Lei Yang. "Drifting Streaming Peaks-over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast." Future Internet 15, no. 1 (2022): 17. http://dx.doi.org/10.3390/fi15010017.

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This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generati

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LANSNER, ANDERS, and ANDERS HOLST. "A HIGHER ORDER BAYESIAN NEURAL NETWORK WITH SPIKING UNITS." International Journal of Neural Systems 07, no. 02 (1996): 115–28. http://dx.doi.org/10.1142/s0129065796000816.

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We treat a Bayesian confidence propagation neural network, primarily in a classifier context. The onelayer version of the network implements a naive Bayesian classifier, which requires the input attributes to be independent. This limitation is overcome by a higher order network. The higher order Bayesian neural network is evaluated on a real world task of diagnosing a telephone exchange computer. By introducing stochastic spiking units, and soft interval coding, it is also possible to handle uncertain as well as continuous valued inputs.

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Du, Rei-Jie, Shuang-Cheng Wang, Han-Xing Wang, and Cui-Ping Leng. "Optimization of Dynamic Naive Bayesian Network Classifier with Continuous Attributes." Advanced Science Letters 11, no. 1 (2012): 676–79. http://dx.doi.org/10.1166/asl.2012.2965.

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Thèses sur le sujet "Continuous Time Bayesian Network Classifier"

CODECASA, DANIELE. "Continuous time bayesian network classifiers." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/80691.

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Streaming data are relevant to finance, computer science, and engineering, while they are becoming increasingly important to medicine and biology. Continuous time Bayesian networks are designed for analyzing efficiently multivariate streaming data, exploiting the conditional independencies in continuous time homogeneous Markov processes. Continuous time Bayesian network classifiers are a specialization of continuous time Bayesian networks designed for multivariate streaming data classification when time duration of events matters and the class occurs in the future. Continuous time Bayesian net

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VILLA, SIMONE. "Continuous Time Bayesian Networks for Reasoning and Decision Making in Finance." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/69953.

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L'analisi dell'enorme quantità di dati finanziari, messi a disposizione dai mercati elettronici, richiede lo sviluppo di nuovi modelli e tecniche per estrarre efficacemente la conoscenza da utilizzare in un processo decisionale informato. Lo scopo della tesi concerne l'introduzione di modelli grafici probabilistici utilizzati per il ragionamento e l'attività decisionale in tale contesto. Nella prima parte della tesi viene presentato un framework che utilizza le reti Bayesiane per effettuare l'analisi e l'ottimizzazione di portafoglio in maniera olistica. In particolare, esso sfrutta, da un l

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Fan, Yu. "Continuous time Bayesian Network approximate inference and social network applications." Diss., [Riverside, Calif.] : University of California, Riverside, 2009. http://proquest.umi.com/pqdweb?index=0&did=1957308751&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268330625&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2009.<br>Includes abstract. Title from first page of PDF file (viewed March 8, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 130-133). Also issued in print.

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ACERBI, ENZO. "Continuos time Bayesian networks for gene networks reconstruction." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/52709.

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Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and improve the models' expressiveness. Continuous time Bayesian network

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Chapitres de livres sur le sujet "Continuous Time Bayesian Network Classifier"

Codecasa, Daniele, and Fabio Stella. "A Classification Based Scoring Function for Continuous Time Bayesian Network Classifiers." In New Frontiers in Mining Complex Patterns. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08407-7_3.

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Shi, Dongyu, and Jinyuan You. "Update Rules for Parameter Estimation in Continuous Time Bayesian Network." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_17.

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Acerbi, Enzo, and Fabio Stella. "Continuous Time Bayesian Networks for Gene Network Reconstruction: A Comparative Study on Time Course Data." In Bioinformatics Research and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08171-7_16.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Probabilistic Graphical Model for Continuous Variables." In Intelligent Control and Learning Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_14.

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AbstractMost of the sampled data in complex industrial processes are sequential in time. Therefore, the traditional BN learning mechanisms have limitations on the value of probability and cannot be applied to the time series. The model established in Chap. 10.1007/978-981-16-8044-1_13 is a graphical model similar to a Bayesian network, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the continuous process variables, which avoids the assumption of discrete or Gaussian distributions.

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Aliferis, Constantin, and Gyorgy Simon. "Foundations and Properties of AI/ML Systems." In Health Informatics. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-39355-6_2.

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AbstractThe chapter provides a broad introduction to the foundations of health AI and ML systems and is organized as follows: (1) Theoretical properties and formal vs. heuristic systems: computability, incompleteness theorem, space and time complexity, exact vs. asymptotic complexity, complexity classes and how to establish complexity of problems even in the absence of known algorithms that solve them, problem complexity vs. algorithm and program complexity, and various other properties. Moreover, we discuss the practical implications of complexity for system tractability, the folly of expecti

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Flores, M. Julia, José A. Gámez, and Ana M. Martínez. "Supervised Classification with Bayesian Networks." In Intelligent Data Analysis for Real-Life Applications. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1806-0.ch005.

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Bayesian Network classifiers (BNCs) are Bayesian Network (BN) models specifically tailored for classification tasks. There is a wide range of existing models that vary in complexity and efficiency. All of them have in common the ability to deal with uncertainty in a very natural way, at the same time providing a descriptive environment. In this chapter, the authors focus on the family of semi-naïve Bayesian classifiers (naïve Bayes, AODE, TAN, kDB, etc.), motivated by the good trade-off between efficiency and performance they provide. The domain of the BNs is generally of discrete nature, but

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Veetil, Sanjai, and Qigang Gao. "Real-time Network Intrusion Detection Using Hadoop-Based Bayesian Classifier." In Emerging Trends in ICT Security. Elsevier, 2014. http://dx.doi.org/10.1016/b978-0-12-411474-6.00018-9.

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Chakraborty, Chinmay, Bharat Gupta, and Soumya K. Ghosh. "Chronic Wound Characterization Using Bayesian Classifier under Telemedicine Framework." In Medical Imaging. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch030.

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Chronic wound (CW) treatment by large is a burden for the government and society due to its high cost and time consuming treatment. It becomes more serious for the old age patient with the lack of moving flexibility. Proper wound recovery management is needed to resolve this problem. Careful and accurate documentation is required for identifying the patient's improvement and or deterioration timely for early diagnostic purposes. This paper discusses the comprehensive wound diagnostic method using three important modules, viz. Wounds Data Acquisition (WDA) module, Tele-Wound Technology Network

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Beaudry Éric, Kabanza Froduald, and Michaud François. "Planning with Concurrency under Resources and Time Uncertainty." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-606-5-217.

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Planning with actions concurrency under resources and time uncertainty has been recognized as a challenging and interesting problem. Most current approaches rely on a discrete model to represent resources and time, which contributes to the combinatorial explosion of the search space when dealing with both actions concurrency and resources and time uncertainty. A recent alternative approach uses continuous random variables to represent the uncertainty on time, thus avoiding the state-space explosion caused by the discretization of timestamps. We generalize this approach to consider uncertainty

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Chakraborty, Chinmay, Bharat Gupta, and Soumya K. Ghosh. "Identification of Chronic Wound Status under Tele-Wound Network through Smartphone." In E-Health and Telemedicine. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8756-1.ch037.

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This paper presents a tele-wound framework for monitoring chronic wound status based on color variation over a period of time. This will facilitate patients at remote locations to connect to medical experts through mobile devices. Further this will help medical professionals to monitor and manage the wounds in more timely, accurate and precise manner using the proposed framework. Tele-medical agent (TMA) collects the chronic wound data using smart phone and send it to the Tele-medical hub (TMH). In TMH, the wound image has been segmented using Fuzzy C-Means which gives highest segmented accura

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Actes de conférences sur le sujet "Continuous Time Bayesian Network Classifier"

Li, Zhengquan, Qingzhen Meng, Zhouhui Shen, Long Chen, and Ye Xia. "Single-spot Damage Assessment of Concrete-filled Steel Tubular Structures through Convolutional Neural Network-based Time Series Analysis." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0322.

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<p>For medium to large bridges, structural health monitoring systems often require the deployment of multiple embedded sensors. However, it is difficult to ensure that sensors cover all regions of the bridge during actual service. To tackle the issue, a non-contact sensor-assisted approach for single- spot disease assessment of concrete-filled steel tubular (CFST) structures is proposed in this paper. Acoustic signals from CFST structures at various damage levels were acquired using percussion excitation and subsequently pre-processed with continuous wavelet transform. On this basis, a t

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Villa, Simone, and Fabio Stella. "Learning Continuous Time Bayesian Networks in Non-stationary Domains." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/804.

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Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.

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Matthews, Jordan, Timothy Klatt, Carolyn C. Seepersad, Michael Haberman, and David Shahan. "Hierarchical Design of Composite Materials With Negative Stiffness Inclusions Using a Bayesian Network Classifier." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13128.

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Recent research in the field of composite materials has shown that it is theoretically possible to produce composite materials with macroscopic mechanical stiffness and loss properties that surpass those of conventional composites. This research explores the possibility of designing and fabricating these composite materials by embedding small volume fractions of negative stiffness inclusions in a continuous host material. Achieving high stiffness and loss from these materials by design, however, is a nontrivial task. This paper presents a hierarchical multiscale material model for these materi

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Wiest, Tyler, Carolyn Conner Seepersad, and Michael Haberman. "Design Space Exploration in Sparse, Mixed Continuous/Discrete Spaces via Synthetically Enhanced Classification." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85274.

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Exploration of a design space is the first step in identifying sets of high-performing solutions to complex engineering problems. For this purpose, Bayesian network classifiers (BNCs) have been shown to be effective for mapping regions of interest in the design space, even when those regions of interest exhibit complex topologies. However, identifying sets of desirable solutions can be difficult with a BNC when attempting to map a space where high-performance designs are spread sparsely among a disproportionately large number of low-performance designs, resulting in an imbalanced classifier. I

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Choi, YooJung, Adnan Darwiche, and Guy Van den Broeck. "Optimal Feature Selection for Decision Robustness in Bayesian Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/215.

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In many applications, one can define a large set of features to support the classification task at hand. At test time, however, these become prohibitively expensive to evaluate, and only a small subset of features is used, often selected for their information-theoretic value. For threshold-based, Naive Bayes classifiers, recent work has suggested selecting features that maximize the expected robustness of the classifier, that is, the expected probability it maintains its decision after seeing more features. We propose the first algorithm to compute this expected same-decision probability for g

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Santoso, Ryan, Xupeng He, Marwa Alsinan, Hyung Kwak, and Hussein Hoteit. "Bayesian Long-Short Term Memory for History Matching in Reservoir Simulations." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203976-ms.

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Abstract History matching is critical in subsurface flow modeling. It is to align the reservoir model with the measured data. However, it remains challenging since the solution is not unique and the implementation is expensive. The traditional approach relies on trial and error, which are exhaustive and labor-intensive. In this study, we propose a new workflow utilizing Bayesian Markov Chain Monte Carlo (MCMC) to automatically and accurately perform history matching. We deliver four novelties within the workflow: 1) the use of multi-resolution low-fidelity models to guarantee high-quality matc

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Zhou, Andrew, and Ivan Revilla. "A Cost-Effective Virtual Sensor for Continuous Freshwater Nutrient Monitoring using Machine Learning." In 10th International Conference on Artificial Intelligence & Applications. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.131912.

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Nutrient enrichment of aquatic environments is a prevalent issue with wide-reaching negative implications for ecological stability, tourism and recreation, and vital drinking supplies. Proper management of nutrient influxes—primarily nitrogen and phosphorus—into aquatic environments is facilitated by continuous monitoring of nutrient levels within water bodies of interest, which offers a more complete understanding of seasonal trends and faster response times compared to traditional lab testing. However, continuous nutrient monitoring systems are prohibitively expensive, with ongoing energy an

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Nannapaneni, Saideep, Sankaran Mahadevan, and Abhishek Dubey. "Real-Time Control of Cyber-Physical Manufacturing Process Under Uncertainty." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6460.

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Modern manufacturing processes are increasing becoming cyber-physical in nature, where a computational system monitors the system performance, provides real-time process control by analyzing sensor data collected regarding process and product characteristics, in order to increase the quality of the manufactured product. Such real-time process monitoring and control techniques are useful in precision and ultra-precision machining processes. However, the output product quality is affected by several uncertainty sources in various stages of the manufacturing process such as the sensor uncertainty

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Heidari, Hojat, and Abdolreza Ohadi. "Fault Detection in Gearbox With Non-Stationary Rotational Speed Using CWT Feature Extraction, PCA Reduction and ANN Classifier Methods." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71271.

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Gearbox fault diagnosis is one of the core research areas in the field of condition monitoring of rotating machines. The aim of this paper is to present an intelligent method for fault diagnosis of a kind of automotive gearbox in run-up condition based on vibration signals. The vibration signals are obtained from an acceleration sensor and sampled at constant time increment by AdvantechTM PCI-1712 card. Automotive gearbox test setup has been designed and constructed in Acoustics Research Laboratory in Amirkabir University of Technology. To process the non-stationary vibration signals, the re-s

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ZENG, JICE, MICHAEL D. TODD, and HU ZHEN. "DEGRADATION MODEL UPDATING FOR FAILURE PROGNOSTICS USING A SEQUENTIAL LIKELIHOOD- FREE BAYESIAN INFERENCE METHOD AND VIDEO MONITORING DATA." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36804.

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Structural systems are inevitably subject to degradation that evolves progressively over time. Developing a degradation model to capture the physics of damage evolution is essential for failure prognostics, i.e., remaining useful life (RUL) prediction, to enable individualized predictive maintenance. Due to the lack of runto- failure data for large structural systems and natural variability across physical systems, uncertainty is inherent in the degradation model even if a degradation model can be constructed based on the physics of a certain damage mechanism. It is therefore necessary to upda

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